Number of deaths registered each month by area of usual residence for England and Wales, by region, county, health authorities, local and unitary authority, and London borough.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

One in every 100 children dies before completing one year of life. Around 68 percent of infant mortality is attributed to deaths of children before completing 1 month. 15,000 children die every day – Child mortality is an everyday tragedy of enormous scale that rarely makes the headlines Child mortality rates have declined in all world regions, but the world is not on track to reach the Sustainable Development Goal for child mortality Before the Modern Revolution child mortality was very high in all societies that we have knowledge of – a quarter of all children died in the first year of life, almost half died before reaching the end of puberty Over the last two centuries all countries in the world have made very rapid progress against child mortality. From 1800 to 1950 global mortality has halved from around 43% to 22.5%. Since 1950 the mortality rate has declined five-fold to 4.5% in 2015. All countries in the world have benefitted from this progress In the past it was very common for parents to see children die, because both, child mortality rates and fertility rates were very high. In Europe in the mid 18th century parents lost on average between 3 and 4 of their children Based on this overview we are asking where the world is today – where are children dying and what are they dying from?

5.4 million children died in 2017 – Where did these children die? Pneumonia is the most common cause of death, preterm births and neonatal disorders is second, and diarrheal diseases are third – What are children today dying from? This is the basis for answering the question what can we do to make further progress against child mortality? We will extend this entry over the course of 2020.

@article{owidchildmortality, author = {Max Roser, Hannah Ritchie and Bernadeta Dadonaite}, title = {Child and Infant Mortality}, journal = {Our World in Data}, year = {2013}, note = {https://ourworldindata.org/child-mortality} }

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

Title: Monthly War Data on WWI and WWII (Synthetic)

Description: This dataset provides a synthetic month-by-month, country-level representation of key metrics during World War I and World War II. It encompasses key nations involved in the conflicts and aims to showcase patterns and events influenced by historical timelines.

Key Features:

Year: The year of the data entry. Month: The month of the data entry. Country: The nation in focus. The dataset includes the USA, UK, Germany, USSR/Russia, and France. War Status: A binary variable indicating if the war was ongoing for that nation during that month (1 for Yes, 0 for No). Civilian Deaths: An estimated count of civilian deaths during that month. Military Deaths: An estimated count of military deaths during that month. Economic Impact Factor: A fictional index from 0 to 100 indicating the economic strain on the nation (a higher score indicates more strain). Population: Estimated population of the nation during that month. Note:

The data provided in this dataset is synthetically generated and is influenced by historical events and timelines. However, it is not an accurate representation of actual events and should be used with caution for analytical purposes. It is primarily designed for educational and illustrative tasks, allowing users to practice data analysis techniques in a historically-inspired context.

Excess Winter Deaths (EWD) by age and conditions (underlying cause of death) expressed as average per year based on 7 years pooled data, 2004-2011. EWD trend expressed as average per year based on 3 years data. The Excess Winter Mortality Index (EWM Index was calculated based on the 'ONS Method' which defines the winter period as December to March, and the non-winter period as August to November of that same year and April to July of the following year. This winter period was selected as they are the months which over the last 50 years have displayed above average monthly mortality. However, if mortality starts to increase prior to this, for example in November, the number of deaths in the non-winter period will increase, which in turn will decrease the estimate of excess winter mortality. The EWM Index will be partly dependent on the proportion of older people in the population as most excess winter deaths effect older people (there is no standardisation in this calculation by age or any other factor). Excess winter mortality is calculated as winter deaths (deaths occurring in December to March) minus the average of non-winter deaths (April to July of the current year and August to November of the previous year). The Excess winter mortality index is calculated as excess winter deaths divided by the average non-winter deaths, expressed as a percentage. Relevant link: http://www.wmpho.org.uk/excesswinterdeathsinEnglandatlas/

Provisional counts of deaths by the month the deaths occurred, by age group and race/ethnicity, for select underlying causes of death for 2020-2021. Final data is provided for 2019. The dataset also includes monthly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

What Age Groups are dying from COVID Alone?

What races suffer from COVID 19 most?

Are deaths from COVID-19 Decreasing?

https://healthdata.gov/dataset/AH-Monthly-Provisional-Counts-of-Deaths-for-Select/bj4f-mcqz

The Excess Winter Mortality Index (EWD Index) shows excess winter deaths as a Percentage Ratio of the number of deaths expected in the (eight) warmer months either side of Winter (01 December to 31 March). So the data’s yearly time period is from 01 August to 31 July the following year. In other words, EWD is the ratio of extra deaths from all causes during the winter months compared to average non-winter deaths. The EWD Index is partly dependent on the proportion of Older People in the population, as most excess winter deaths affect Older People. This indicator covers all ages, but there is no standardisation in its calculation by age or any other factor. So figures for an area can be influenced for example by the proportion of Older People. This dataset is updated annually. Source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF), indicator 90360 / E14. Age breakouts, confidence intervals and metadata are shown on the PHE (PHOF) site. Note: Please be advised that the ONS currently has this dataset under consultation for review (as of 09/01/2025) so may not be updated annually until the review has concluded. The full notice can be found on the ONS link for the Winter Mortality publication - please see link in the Additional Information Section.

A. SUMMARY This dataset includes data from the Office of the Chief Medical Examiner on the number of preliminary unintentional fatal drug overdoses per month. B. HOW THE DATASET IS CREATED The Office of the Chief Medical Examiner releases a monthly report containing the previous month’s preliminary count of unintentional fatal drug overdoses. This dataset is manually updated based on that report. The San Francisco Office of the Chief Medical Examiner (OCME) investigates any unknown cause of death for deaths that occur in San Francisco. OCME uses drug testing, death scene investigation, autopsy, medical record, and informant information to determine the cause of death. Preliminary determinations are generally based on drug testing and death scene investigations. Preliminary deaths reported by the medical examiner consist of two categories: (a) cases that are still under investigation and involve suspected acute toxicity from opioids, cocaine, or methamphetamine; and (b) cases that have been finalized and were attributed to acute toxicity from any substance (including prescribed medication and over-the-counter medication). C. UPDATE PROCESS This dataset is updated monthly following the release of the monthly accidental fatal drug overdose report from the Office of the Chief Medical Examiner. Department of Public Health staff manually copy data from the Office of the Chief Medical Examiner’s report to update this dataset. D. HOW TO USE THIS DATASET This dataset is updated each month to include the most recent month’s preliminary accidental fatal drug overdose count. Counts from previous months are often also updated as it can take more than a month for the Office of the Chief Medical Examiner to finish reviewing cases. E. RELATED DATASETS San Francisco Department of Public Health Substance Use Services Overdose-Related 911 Responses by Emergency Medical Services (EMS) Unintentional Drug Overdose Death Rate by Race/Ethnicity

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

The Colorado Death Tables dataset contains data and statistics as published to the Viral Respiratory Diseases website for deaths due to COVID-19 per county. The data in this file updates each Wednesday and includes the following fields for Colorado resident demographic data and total deaths due to COVID-19 in the State of Colorado reported from Colorado Vital Statistics by month from January 2020 through the most current date:section: (Death)subsection: (Age, Historical Trends, Month, Pediatric Deaths, Race/Ethnicity, Sex, Summary)level: (Statewide)metric: (definitions corresponding to published demographic values)pathogen: (COVID-19)date: (By Month)mmwr_week: (By Month)countrate (count/denominator from Colorado Dept of Local Affairs)publish_date (date that all of the published values in this dataset were calculated/assembled and published)For more information, data definitions, and context, please visit Colorado’s Viral Respiratory Diseases data website (https://cdphe.colorado.gov/viral-respiratory-diseases-report).

Dataset from Ministry of Health. For more information, visit https://data.gov.sg/datasets/d_abeeab6fb3b739d7b234e7452bafd07c/view

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency.

The California Department of Public Health (CDPH) is identifying vaccination status of COVID-19 cases, hospitalizations, and deaths by analyzing the state immunization registry and registry of confirmed COVID-19 cases. Post-vaccination cases are individuals who have a positive SARS-Cov-2 molecular test (e.g. PCR) at least 14 days after they have completed their primary vaccination series.

Tracking cases of COVID-19 that occur after vaccination is important for monitoring the impact of immunization campaigns. While COVID-19 vaccines are safe and effective, some cases are still expected in persons who have been vaccinated, as no vaccine is 100% effective. For more information, please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Post-Vaccine-COVID19-Cases.aspx

Post-vaccination infection data is updated monthly and includes data on cases, hospitalizations, and deaths among the unvaccinated and the vaccinated. Partially vaccinated individuals are excluded. To account for reporting and processing delays, there is at least a one-month lag in provided data (for example data published on 9/9/22 will include data through 7/31/22).

Notes:

• On September 9, 2022, the post-vaccination data has been changed to compare unvaccinated with those with at least a primary series completed for persons age 5+. These data will be updated monthly (first Thursday of the month) and include at least a one month lag.

• On February 2, 2022, the post-vaccination data has been changed to distinguish between vaccination with a primary series only versus vaccinated and boosted. The previous dataset has been uploaded as an archived table. Additionally, the lag on this data has been extended to 14 days.

• On November 29, 2021, the denominator for calculating vaccine coverage has been changed from age 16+ to age 12+ to reflect new vaccine eligibility criteria. The previous dataset based on age 16+ denominators has been uploaded as an archived table.

Here you will find an open data set with the Labour Inspection Authority’s statistics on occupational injury deaths per year for the last five-year period. The Working Environment Act & 5-2 requires employers to notify the Labour Inspection Authority of serious work-related personal injuries to their own employees. Occupational injury death means a work injury that causes the injured employee to die within one year of the accident. The Labour Inspection Authority provides statistics on occupational injury deaths occurring within the Labour Inspection Authority’s administrative area that is limited to the land-based labour market in Norway. Occupational injury deaths in aviation, shipping, fishing and capture, petroleum activities on the Norwegian continental shelf and the construction and operation of land-based petroleum facilities are followed up by other supervisory authorities. Occupational injury deaths in these industries are therefore not included in these statistics. Occupational injury deaths in military occupations are included, with the exception of deaths in war situations. For more information about the data set read here. The open data set consists of: Year (Ar), Monthly name (Maned), Number of occupational injury deaths (Number)

Dataset title: Deaths from all causes in Western Europe by month, 1914-1918 Related publication: More, A. F. et al. (2020). The impact of a six-year climate anomaly on the ‘Spanish Flu’ Pandemic and WWI. GeoHealth, American Geophysical Union. Figures 2 and 3. Dataset source: Bunle, H. (1954). Le Mouvement naturel de la population dans le monde de 1906 à 1936. Paris, Institut national d’études démographiques, pp. 432-438. N.B. Please cite the original source if you use this dataset. N.B. Please note that Bunle did not publish mortality statistics for Belgium, Bulgaria, and several other countries for the period 1914-20 due to his inability to find reliable sources, as indicated in his footnotes and on p. 12. This dataset includes countries of western Europe with the most reliable data. Units: Thousands of deaths. Each monthly figure should be multiplied by 1000 to obtain the total deaths for a specific month. Each year is divided in 12 monthly entries, with decimals increasing by 0.083 (1/12) for each month.

Content

This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

Several data quality metrics, including the percent completeness in overall death reporting, percentage of deaths with cause of death pending further investigation, and the percentage of drug overdose deaths with specific drugs or drug classes reported are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical notes). Reporting of the specific drugs and drug classes involved in drug overdose deaths varies by jurisdiction, and comparisons of death rates involving specific drugs across selected jurisdictions should not be made (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data...

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

this graph was created in OurDataWorld:

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Think about someone dying from extreme temperatures. You probably pictured someone passing out from heat stroke or dying from hypothermia.

But this is not how most people die from “heat”. They die from conditions such as cardiovascular or kidney disease, respiratory infections, or diabetes.1

Almost no one has “heat” or “cold” written on their death certificate, but sub-optimal temperatures lead to a large number of premature deaths. As we’ll see later, researchers estimate that it kills several million every year.

Older populations are usually most vulnerable to extreme temperatures. Most deaths occur in people older than 65. It’s important to consider what "death" means here and how deaths from extreme temperatures might compare to other causes. Being too hot or cold can increase our risk of developing certain health conditions or worsen existing ones. It can thereby lead to an earlier death than would have occurred if the temperatures were “optimal”.

How much time do hot or cold conditions take off someone’s life? It’s difficult to give precise estimates. One method that researchers often use is to look at excess death rates — which measure how many more people die in a given year compared to an “average” year — in a particularly warm or cold year. Looking at patterns of excess deaths gives some indication of whether temperature-related deaths were “brought forward” significantly or not.

A study by Nirandeep Rehill and colleagues examined death patterns in the United Kingdom over 50 years.2 It found that most cold-related deaths were among people who would not have died in the next 6 months. A later study looked at the impacts of high and low temperatures across a much larger sample of countries.3 It found that most temperature-related deaths reduced lifespans for at least one year. Most people died at least one year earlier, although there would be some that did lose less than this.

In this article, I will examine how many people die from heat and cold each year and how researchers estimate these numbers. In a follow-up article, I’ll look at how these risks could change in the future due to climate change.

A quick note on terminology: I will use the term “temperature-related deaths” from this point forward to refer to the combination of deaths from heat and cold conditions. When I use the term “heat”, I mean warm or hot.